One Shared LoRA Weight for MRI Reconstruction across Acceleration Factors

The paper introduces Shared LoRA, a parameter‑efficient approach for accelerated MRI reconstruction that uses a single set of LoRA adapters and a lightweight gating network to handle multiple acceleration factors. By freezing a pretrained SHFormer backbone and training the adapters on randomly sampled acceleration factors, the method learns reconstruction knowledge across factors. Experiments demonstrate that Shared LoRA achieves competitive PSNR and SSIM while using only about 5.3% of the total model parameters, and it generalizes well to unseen neighboring factors.

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• fine-tuning